Noisy Iris Recognition Based on Deep Neural Network

Omran, Eman M.; Soliman, Randa F.; Salah, Maryam Mostafa; Napoleon, Sameh A.; El-Rabaie, El-Sayed M.; AbdeElnaby, Mustafa M.; Ismail, Nabil A.; Eisa, Ayman A.; Abd El-samie, Fathi

doi:10.21608/mjeer.2020.103276

Noisy Iris Recognition Based on Deep Neural Network

Document Type : Original Article

Authors

¹ A National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo 11787, Egypt.

² Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

³ Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Menouf 32951, Menoufia University.

⁴ Department of Electronics and Communications Engineering, Tanta University, Egypt

⁵ Computer Science & Engineering Dept., Menoufia University, Egypt.

⁶ Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt

10.21608/mjeer.2020.103276

Abstract

Iris recognition is one of the Biometric systems used for persons identification based on their special iris traits, which are unique featuresfor each individual. It is clear that the progress in deep learning show how efficient the extracted features from convolutional neural networks (CNNs) to describe the complex image patterns. However, the influence of noise is a serious problem in most image processing systems. It may ariseto the iris recognition systems due to environmental conditions that can affects the features extracted from the iris images. Hence, the objective of this paper is to study the performance of CNNs based Deep learning (Alex net, Vgg16 and Vgg19) when used for iris recognition with the presence of noise and compares it with Masek algorithm. Simulation results reveal that using the deep learning greatly improves iris recognition accuracy for Alex CNN. We achieve 100%, 100%, 88.9% for interval, lamp and twins datasets respectively.

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